Why are AI's data requirements identical to Tier 3's architectural properties?

The architectural-mapping question §10.2 raises. If the AI strategic position is to be captured as a byproduct of Tier 3 deployment, the data requirements of defensible AI development have to map structurally onto Tier 3's architectural properties.

The answer is C-0008, organized through M-0006 (the AI-Data ↔ Tier-3 Mapping):

• Provenance (what data trained a model and where it came from) is the structural product of content addressing and signed deposit (M-0002, §2.1, §8.2).
• Reproducibility (re-run a training pipeline against the original corpus) requires the corpus to persist across the lifetime of any model trained on it — exactly the preservation property Tier 3 produces (§6.2, §9).
• Federation (train across institutional boundaries without consolidating sensitive data into a single trust domain) is the operational pattern §7.6 documents for permissioned BitTorrent, federated Matrix, and permissioned IPFS clusters; the same architecture HIPAA-, FERPA-, and export-controlled research already requires (C-0015).
• Verification (demonstrate to a regulator, court, or peer reviewer that training data was what the model card claims) is the architectural property C-0007 develops: a single cryptographic query produces evidence any third party can independently re-verify.

The four properties map one-to-one. The institutions that operate Tier 3 nodes for the §§2-9 reasons hold the AI-ready data substrate as a byproduct. The infrastructure investment that hedges the §5 liability also captures the AI dimension; the deployment is two-sided rather than a one-sided hedge.